IHBA-optimized DR-SE-NPCNet for robust open-circuit fault diagnosis in three-level NPC inverters under mixed and noisy conditions

Open-circuit faults (OCFs) in three-level neutral-point-clamped (NPC) inverters can severely degrade power quality and compromise system reliability. However, existing diagnostic methods often exhibit performance degradation under mixed operating conditions and strong noise, and they remain highly sensitive to hyperparameter settings. To address these issues, this paper proposes an integrated model-optimization framework that couples a lightweight diagnostic network, DR-SE-NPCNet, with an improved honey Badger algorithm (IHBA) for global hyperparameter tuning. DR-SE-NPCNet preserves full temporal resolution through a temporal resolution preserving-temporal dilation (TRP-TD) backbone and enhances discriminative representations using a residual and squeeze-and-excitation-calibrated fusion (ReSE-CF) module. IHBA further stabilizes and improves the model by enabling efficient and robust hyperparameter optimization. Experiments on a hardware NPC inverter platform demonstrate that the proposed method achieves 92.83-96.94% accuracy under 10 dB noise and mixed variations in load level, modulation index, DC-bus voltage, and output frequency, outperforming conventional CNN-based approaches. With IHBA optimization, diagnostic accuracy is further increased by an additional 2-3%. These results confirm that the integrated DR-SE-NPCNet and IHBA framework provides a robust and high-accuracy solution for OCF diagnosis under severe noise and mixed operating conditions.